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The emotional content of several databases are annotated with continuous-time (CT) annotations, providing traces with frame-by-frame scores describing the instantaneous value of an emotional attribute. However, having a single score describing the global emotion of a short segment is more convenient for several emotion recognition formulations. A common approach is to derive sentence-level (SL) labels from CT annotations by aggregating the values of the emotional traces across time and annotators. How similar are these aggregated SL labels from labels originally collected at the sentence level? The release of the MSP-Podcast (SL annotations) and MSP-Conversation (CT annotations) corpora provides the resources to explore the validity of aggregating SL labels from CT annotations. There are 2,884 speech segments that belong to both corpora. Using this set, this study (1) compares both types of annotations using statistical metrics, (2) evaluates their inter-evaluator agreements, and (3) explores the effect of these SL labels on speech emotion recognition (SER) tasks. The analysis reveals benefits of using SL labels derived from CT annotations in the estimation of valence. This analysis also provides insights on how the two types of labels differ and how that could affect a model. [ABSTRACT FROM AUTHOR]

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In the automotive industry, products must be tested and verified before being launched; currently, products such as automotive radio are usually tested with expensive metrology equipment, such as the Rohde & Schwarz SFE100, which produces radio frequency signals. This study assessed the feasibility of replacing the SFE100 with two inexpensive devices, Adalm-Pluto and LimeSDR usb, which are software-defined radios. For this evaluation, a study was carried out to determine specific metrological characteristics and then to compare with those of metrological equipment. Based on the results and tests of the study, we conclude that SDRs can generate test signals (FM, DAB, DVB, ISDB and SXM) with uncertainties of 0.5dBm and a spectral density ratio comparable to SFE100, thereby enabling their replacement. Furthermore, the evaluation datasets of all equipment have been publicly available [1] , enabling the scientific community to extract the metrological uncertainties for all signal carriers generated by the device. The result of this study proposes a methodology for testing auto radios that is innovative and can be applied to a production line based on the principles of Industry 4.0. [ABSTRACT FROM AUTHOR]

With convergence of broadband access, computing and telephony, smartphones have empowered listeners to break free from the constraints of schedules and take control of their listening experiences. As a result, podcasts have gained popularity globally and many new people are finally tuning in. In the United Arab Emirates (UAE), the podcast community thrives with stories ranging from tech, culture and music to Middle East affairs, hosted by a diverse group of expatriates and local Emiratis. This study investigates the burgeoning podcast landscape in UAE, examining content creators' motivations, challenges and opportunities in this emerging media space. Through in-depth interviews with key stakeholders, including pioneers and newcomers in the UAE podcasting industry, the research explores factors driving podcasting's rise in the region, such as the need for localized content, the changing media landscape and the significance of community-building and interactivity in podcast creation and distribution. Furthermore, the study delves into podcasting as a horizontal medium, offering insights into how content creators push the boundaries of democratized culture on the World Wide Web. The findings highlight growth and monetization potential within the UAE podcasting market and the challenges and opportunities presented by decentralized podcast distribution. Finally, the research touches upon regulation and standardization in podcasting, examining the freedom of expression afforded by this relatively unregulated medium compared to traditional media forms. Overall, this study provides valuable insights into the emerging podcasting landscape in the UAE and sets the stage for future research exploring the motivations and challenges faced by individual creators and podcast networks in the region. [ABSTRACT FROM AUTHOR]

The main issue in the development of precise positioning is the way in which to send GNSS corrections. To handle this case, a terrestrial broadcast mechanism based on DAB+ (digital audio broadcasting) signals was chosen to overcome the scalability challenges, as it would allow a service provider to transfer assistance data based on a "one-to-many" scheme. DAB is easily widely deployed, with large coverage predictions carried using ATDI's HTZ simulator. The DAB+ reception demonstrator uses SSRZ, a compressed and compact state space representation corrections format developed by Geo++. SSRZ corrections were generated using data from Teria's CORS network and have been broadcasted over the air with DAB+ signals. To validate the SSRZ corrections, the "Record&Replay" technique was used. It consists of digitizing GNSS signals during a normal field test. Once data were collected and validated, replays were executed in the laboratory. Different scenarios were set up to validate the accuracy of the position with SSRZ corrections sent via DAB+ and the convergence to the corrected position after ambiguity loss. The results show that the accuracy was acceptable when the user remained close to the transmitting DAB+ antenna (<10 km). The lower performance might result from broadcast limitations in urban environments. [ABSTRACT FROM AUTHOR]

In October 2018, local digital radio was launched to cover the agglomeration of Wroclaw. The implementation of this undertaking required many tests, including qualitative ones, that refer to both music and speech. This paper presents the results of subjective tests based on the evaluation of speech quality of signals recorded at various points in Wroclaw. Measurements were carried out in accordance with the recommendations of the International Telecommunication Union as well as in ordinary acoustic conditions in listeners’ flats. The rating was made for male and female voices. The most important conclusion is that for speech signal assessment in meaning of the quality the test conditions do not influence the obtained results. The other fact confirmed in the experiment was that the receiving place of DAB+ signal in the Single-Frequency Network also does not affect the perceived voice quality.

Self‐attention has been successfully leveraged for long‐range feature‐wise similarities in deep learning super‐resolution (SR) methods. However, most of the SR methods only explore the features on the original scale, but do not take full advantage of self‐similarities features on different scales especially in generative adversarial networks (GAN). In this paper, self‐similarity generative adversarial networks (SSGAN) are proposed as the SR framework. The framework establishes the multi‐scale feature correlation by adding two modules to the generative network: downscale attention block (DAB) and upscale attention block (UAB). Specifically, DAB is designed to restore the repetitive details from the corresponding downsampled image, which achieves multi‐scale feature restoration through self‐similarity. And UAB improves the baseline up‐sampling operations and captures low‐resolution to high‐resolution feature mapping, which enhances the cross‐scale repetitive features to reconstruct the high‐resolution image. Experimental results demonstrate that the proposed SSGAN achieve better visual performance especially in the similar pattern details. [ABSTRACT FROM AUTHOR]

Copyright of Austral Comunicacion is the property of Austral Comunicacion and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In October 2018, local digital radio was launched to cover the agglomeration of Wroclaw. The implementation of this undertaking required many tests, including qualitative ones, that refer to both music and speech. This paper presents the results of subjective tests based on the evaluation of speech quality of signals recorded at various points in Wroclaw. Measurements were carried out in accordance with the recommendations of the International Telecommunication Union as well as in ordinary acoustic conditions in listeners' flats. The rating was made for male and female voices. The most important conclusion is that for speech signal assessment in meaning of the quality the test conditions do not influence the obtained results. The other fact confirmed in the experiment was that the receiving place of DAB+ signal in the Single-Frequency Network also does not affect the perceived voice quality. [ABSTRACT FROM AUTHOR]

This paper presents the research and implementation of a China Digital Radio (CDR) experimental network in Hubei, China with two FM-CDR transmitting stations and four FM-band channels. To study the coverage discontinuity problem in a large service area, the experimental network employs an intelligent networking solution that incorporates multiple service channels carrying Broadcast and Data Services (BADS) and a Common-Regional-Information Channel (CRIC) carrying both service data and system information. The electronic service guide (ESG) carried by CRIC can inform the receivers about the available contents from current and adjacent stations, which can significantly improve the service quality in the overlapping areas, especially under mobile reception environment. Two RF tuners are used in the receiving terminals to access the CRIC, and the BADS channel, respectively. This paper first presents the frequency analysis and coverage tests of the experimental network, and then summarizes the coverage performances with different transmission parameter combinations. The goal is to gain experience in frequency planning, coverage prediction, as well as supportable services of CDR digital audio broadcasting. [ABSTRACT FROM AUTHOR]

With the development of electronics and communication technology, digital audio processing technologies such as digital audio broadcasting and multimedia communication have been widely used in society, and their influence on people's lives has become increasingly profound. At present, the real-time and accuracy of musical instrument tuners on the market need to be improved, which hinders the design of vocal music teaching system. Based on the BP neural network algorithm and fast Fourier transform algorithm in FPGA, this paper designs a real-time and efficient audio spectrum analysis system, which realizes the spectrum analysis function of music signal. The methods to calculate fast discrete Fourier transform are the FFT algorithm based on time extraction and the FFT algorithm based on frequency extraction. The characteristic of BP neural network algorithm is that it can not only obtain the corresponding estimation results by forward propagation of the input data but also carry out back propagation from the output layer according to the error between the estimation results and the actual results, so as to optimize the connection weight between each layer. This paper proposes to add Nios II system to FPGA processor and adopt cyclone IV in the hardware design of the system, which can be better compatible with the system designed in this paper. In the software part, WM8731 is used to process the audio data. WM8731 consumes very little power to the system, which will effectively improve the processing efficiency of the system. Compared with the original system, the data model obtained after screening and processing of the system model designed in this paper has an algorithm accuracy of more than 90%, among which the audio spectrum clarity of vocal music can reach 95%. Based on the above, the circuit of each module is tested, and in the specific experimental process, the audio frequency spectrum under different conditions is analyzed and data processed. The system can complete the collection and analysis of various music signals in real time, overcome the limitation of single function of traditional tuner, improve the utilization rate of tuner and the clarity of timbre, and also tune a variety of musical instruments and greatly improve the intonation of musical instruments and the utilization rate of tuner, which has a certain practical value. [ABSTRACT FROM AUTHOR]

Digital Audio Broadcast (DAB) transmitters can be successfully used as illumination sources in Passive Coherent Location (PCL). However, extending the integration time in such a configuration leads to the occurrence of periodical artifacts in the bistatic range/Doppler plots, resulting from the time structure of the DAB signal. In this paper, we propose some methods of signal preprocessing (based on symbol removal, substitution by noise, and duplication) that operate on the DAB transmission frame level and improve the received signal’s correlation properties. We also demonstrate that two of these methods allow us to avoid the mentioned artifacts and thus to improve the quality of the range/Doppler plots with detection results. We evaluate the performance of the proposed methods using real DAB signals acquired in an experimental PCL platform. We also provide the analysis of the Signal to Noise Ratio (SNR) during the detection of a moving target which shows that the proposed solution, based on symbol duplication, can offer around 3 dB of gain in SNR. Finally, we carry out the computational complexity analysis showing that the proposed method can be implemented with a minimal cost after some optimizations.

Digital Audio Broadcast (DAB) transmitters can be successfully used as illumination sources in Passive Coherent Location (PCL). However, extending the integration time in such a configuration leads to the occurrence of periodical artifacts in the bistatic range/Doppler plots, resulting from the time structure of the DAB signal. In this paper, we propose some methods of signal preprocessing (based on symbol removal, substitution by noise, and duplication) that operate on the DAB transmission frame level and improve the received signal's correlation properties. We also demonstrate that two of these methods allow us to avoid the mentioned artifacts and thus to improve the quality of the range/Doppler plots with detection results. We evaluate the performance of the proposed methods using real DAB signals acquired in an experimental PCL platform. We also provide the analysis of the Signal to Noise Ratio (SNR) during the detection of a moving target which shows that the proposed solution, based on symbol duplication, can offer around 3 dB of gain in SNR. Finally, we carry out the computational complexity analysis showing that the proposed method can be implemented with a minimal cost after some optimizations. [ABSTRACT FROM AUTHOR]